development of clickclinica: a novel smartphone application to …€¦ · development of...

Michael and Geleta BMC Medical Informatics and Decision Making 2013, 13:70http://www.biomedcentral.com/1472-6947/13/70

DATABASE Open Access

Development of ClickClinica: a novel smartphoneapplication to generate real-time global diseasesurveillance and clinical practice dataBenedict Daniel Michael1* and David Geleta2

Abstract

Background: Identification and tracking of important communicable diseases is pivotal to our understanding ofthe geographical distribution of disease, the emergence and spread of novel and resistant infections, and are ofparticular importance for public health policy planning. Moreover, understanding of current clinical practice normsis essential to audit clinical care, identify areas of concern, and develop interventions to improve care quality.However, there are several barriers to obtaining these research data. For example current disease surveillancemechanisms make it difficult for the busy doctor to know which diseases to notify, to whom and how, and are alsotime consuming. Consequently, many cases go un-notified. In addition assessments of current clinical practice aretypically limited to small retrospective audits in individual hospitals.Therefore, we developed a free smartphone application to try to increase the identification of major infectiousdiseases and other acute medical presentations and improve our understanding of clinical practice.

Description: Within the first month there were over 1000 downloads and over 600 specific disease notifications,coming from a broad range of specialities, grades and from all across the globe, including some resourcepoor settings.Notifications have already provided important information, such as new cases of TB meningitis, resistant HIV andrabies, and important clinical information, such as where patient with myocardial infarctions are and are notreceiving potentially life-saving therapy.The database generated can also answer new, dynamic and targeted questions. When a new guideline is released,for example for a new pandemic infection, we can track, in real-time, the global usage of the guideline andwhether the recommendations are being followed. In addition this allows identification of where cases with keymarkers of severe disease are occurring. This is a potential resource for guideline-producing bodies, clinicalgovernance and public health institutions and also for patient recruitment into ongoing studies.

Conclusions: Further parallel studies are needed to assess the clinical and epidemiological utility of novel diseasesurveillance applications, such as this, with direct comparisons made to data collected through routine surveillance routes.Nevertheless, current disease surveillance mechanisms do not always comprehensively and accurately reflect diseasedistribution for many conditions. Smartphone applications, such as ClickClinica, are a novel approach with the potentialto generate real-time disease surveillance data that may augment current methods.

Keywords: Epidemiology, Audit, Clinical practice, Quality improvement, iPhone, Smart phone, Application, Guidelines

* Correspondence: [email protected] of Infection and Global Health, University of Liverpool, 1st Floor,Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UKFull list of author information is available at the end of the article

© 2013 Michael and Geleta; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of theCreative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.

mailto:[email protected]

http://creativecommons.org/licenses/by/2.0

Michael and Geleta BMC Medical Informatics and Decision Making 2013, 13:70 of 9http://www.biomedcentral.com/1472-6947/13/70

BackgroundImproved understanding of the development and distribu-tion of novel and resistant infections requires robust andreadily utilised identification and tracking mechanisms.This is fundamental for adequate public health policyplanning [1]. In addition, a clear understanding of currentclinical practice norms is necessary to audit clinical care,identify areas of concern and develop interventions toimprove care quality [2].Whilst many countries have national disease surveil-

lance programmes for major communicable diseases, or‘notifiable diseases’, the work of our group and othershas identified several areas of sub-optimal notification,even in the developed countries. For example, it isestimated by the Health Protection Agency (HPA), thatfor each laboratory confirmed cases of the majorgastrointestinal infection, norovirus, there are up to afurther 288 unreported cases [3]. Furthermore, a UK-wideprospective longitudinal study found that, for gastrointes-tinal infection overall, for every one case notified tonational surveillance, there were 10 GP consultations and147 community cases [4].With regards to the major neurological infection, there

were only 18 cases of acute encephalitis notified to theHPA in 2007 [5]. However, during a 3 month studyconducted in the same year in only 10 adult hospitals weidentified 13 cases of encephalitis [6]. In addition, thenotification of acute viral meningitis between 2004–2006was approximately 1381 per year [5]. During this sameperiod in a single 950 bed general non-specialist NHSteaching hospital, we identified 68 hospitalised patientswith viral meningitis [7]. If extrapolated across the 159,386NHS beds over this same time period, the estimatednumber of cases would be nearly three times larger(n = 3,802 cases). [8] Indeed several studies have reportedthat the true incidence of meningitis in the UK may be10–14 times higher than notified figures [9-11].Nevertheless, microbiological notification of specific

organisms which have been identified maybe somewhatbetter, as there are clear notification standard operatingprocedures in most laboratories. However, this mayfalsely skew epidemiological data to favour particularorganisms of interest. For example, in 2011, 263 (48.8%)of all acute meningitis notifications to the HPA were dueto Neisseria meningitidis [5]. Whereas, in disease-specific,rather than organism-specific, surveillance programmesin developed countries only around 13% of all meningi-tis cases are due to Neisseria meningitidis [12]. Further-more, overall bacteria may only account for up to 26% ofmeningitis [13].The Centre for Disease Control and Prevention (CDC)

has brought together several reporting systems, includingfor HIV and TB, into a single place through the NationalElectronic Disease Surveillance System (NEDSS) [14].

However, this approach still requires each individual toupload their data and does not collect data automaticallyfrom doctors on the front line of patient care.Suboptimal notification from doctors on the front line

may potentially be due to lack of awareness that thecondition is a notifiable one, lack of knowledge of theprocedure for notification and/or limited resources tofacilitate the notification process, such as limited time.From previous work we have found that simple inter-ventions, such as providing the appropriate bottle forvirus identification or for cancer cell identification withinthe kits doctors use when performing a lumbar puncture,we could increase the identification of viruses and cancercells [15,16]. Although most doctors knew the importanceof sending these types of sample, making it less time-consuming to collect them resulted in real changes inpractice and disease identification.In addition assessments of current clinical practice are

typically limited to small retrospective audits of case notesin individual hospitals. Therefore, they only providedelayed information, which is specific to a locality ratherthan reflecting wider healthcare practice [17].To start to address some of these issues and to enhance

disease surveillance, novel technological approaches, suchas ‘crowdsourcing’ are being increasingly used. For example,two recent papers report attempts to use Google © trendsdata to assess influenza incidence and compared this withCDC data. One identified good correlation between Google© influenza search trends and CDC notifications and oneutilised these data to develop a mathematical model to pre-dict influenza outbreaks 7 weeks prior to the occurrence.[18,19] However, whilst this approach utilises large datasets,which are required to generate such models, the veracity ofthe data is less robust than that submitted by clinicians.Medical technology, such as ‘Apps’ for smartphones

are revolutionising the way doctors practice medicine,increasing dissemination of research and making clinicalguidelines more widely available [20]. However, the majorityof these support only unidirectional traffic of information(e.g. simply dissemination of guidelines or monitoring ofan individual’s health parameters, such as blood pressurerecordings, and relaying this information to their individualdoctor) [21]. A structured Medline search was conductedfor papers between 1985-current in any language using thesearch terms: “smartphone” OR “phone” OR “application”WITH “disease surveillance” OR “epidemiology”. A furthersearch was conducted using the above search terms in theApple App Store ©. However, neither approach identifiedany applications, which allow for the bidirectional flowof data from a central database to clinicians (i.e. clinicalguidelines) and from clinicians to a central database(i.e. disease notifications).Therefore, we set out to develop a novel smartphone

application with bidirectional information traffic to try


to address both of these issues; firstly to improve diseasesurveillance though increasing the notification of majorinfectious and other acute diseases and secondly toimprove our understanding of current clinical practice(https://sites.google.com/site/clickclinicarx/).

Construction and contentThe application brings together the clinical guidelinesfrom across guidelines groups in a single place and ismade freely available for the iPhone. These includeguidelines from large national and international bodiessuch as the National Institute for Health and ClinicalExcellence (NICE), HPA, CDC, the Resuscitation Council,the World Health Organization (WHO) and many others,Depending on the size of each guideline, they are eitherstored in whole or as a clinical summary of the key pointsas a PDF. Therefore, the clinician does not require inter-net access at the time they wish to get the information.Where guidelines were given in summary format or furthermultimedia material is available, a hyperlink to the fullonline guideline is provided. In a similar way to our previousprojects, in which we have found that interventions thatreduce the time required from doctors have the greatestimpact, the app does not require any additional effortfrom the doctor than simply reading the guidelines theywant to read anyway [15,16]. Indeed, by making themreadily available and clearly indexed, it is easier for thedoctor to use the app to read a section of the guidelinesthan it is to perform an internet search, then find thewebsite, then find the document, then download the PDF,then scroll through to find the page of interest.In reading guidelines on the application, the app has

the potential to generate a real-time global diseasesurveillance database based on the usage. Also from abrief question that appears on the bottom of the screenwhen viewing a specific disease, more detailed informationon either important epidemiological or clinical carequality issues can be collected. One example is thatwhen a doctor is viewing a guideline on H1N1 influenza,our central database is made aware that the guideline isbeing read and the doctor’s grade and hospital. Moreover,on clicking yes in the ‘question box’ at the bottom of thescreen, such as “Are you prescribing chemoprophylaxis?”we are immediately also notified that the doctor hasanswered the question and their name, specialty, GPSlocation and, if allowed, email address, medical registrationnumber and also whether the doctor is happy to becontacted for research purposes regarding this case. Inanswering the question this ‘question box’ disappearsproviding more space for the guideline to be viewed(Figure 1). The doctor only needs to input their personalinformation once at the point of downloading the applica-tion (Figure 2). Therefore, the application allows us toimmediately track the distribution of disease, the spread

of outbreaks and their management. The question atthe bottom of the screen is specific for each disease, orsub-section within the disease guideline.For example, for influenza:

� When reviewing the front page, the question is “areyou seeing a patient with H1N1?”

� When reviewing the resistance page within theinfluenza document the question is “does thispatient have resistant virus?”

� When reviewing the treatment table within theinfluenza document the question is “are youprescribing antivirals?”

Whilst these questions are currently set centrally bythe ClickClinica team, as the project expands we arecollaborating with other research institutions to use ourplatform for them to include research questions of interestto their particular field. The data collected regarding thisis made freely available to them to access in real-timethrough a dedicated terminal, including questions submittedby users. When the app is used in a situation where aclinician does not have an internet connection (be thatWi-Fi or wired internet) the notification is stored withinthe application and subsequently submitted to the centraldatabase automatically when an internet connection isestablished.By obtaining comprehensive data on the usage of the

app, we are able to include guidelines that reflect usagein future updates. In addition the application contains a‘Flag it up’ button, which allows users to suggest guidelinesthey would like to see in future updates of the application.To encourage use of the application, we have included

guidelines to all the major acute medical presentationsthat are covered in competing paid-for applications andhandbooks. Also, we have made it possible for cliniciansto print, email or bookmark the guidelines directly fromtheir smartphone. This also helps to foster guidelinedissemination and the creation of a personalised app. Toencourage users to answer the research questions wehave included internal ClickClinica © certificates thatcan be used as evidence of involvement in research fortheir portfolio of continuing professional development.Also at any time the user can view their personalisedindex of recorded cases.

Utility and discussionThe database generated from the usage of the app canalso answer new, dynamic and targeted questions. Whena new guideline is released (e.g. for a new pandemicinfection) this app has the potential to track, in real-time,the global uptake and usage of the guideline. We can alsoassess whether the recommendations in that guideline(e.g. vaccine delivery) are being followed and where

https://sites.google.com/site/clickclinicarx/

Figure 1 Guideline and database views, with questions being answered.


Figure 2 Launching the ClickClinica© application and initial registration.


cases with key markers of severe disease (e.g. neuro-logical involvement) are occurring. This is a potentialresource of clinical information not only for guideline-producing bodies but also for clinical governance andpublic health institutions.At the time of writing, the app has only been live for

just over 4 weeks and has already received over 1000downloads and over 600 specific disease notifications.The app did not receive any external funding and didnot undergo a marketing campaign. Potential users weremade aware of the app through the Apple App Store,within the ‘free medical applications’ category. In aneffort to reduce spurious notifications, the app wasdesigned such that it is equally efficient for the user

remove the ‘question box’ from the screen by choosingto not notify as it is if they notify. Of the notifications,578 (96%) have included an email address and 405 (70%)of these notifications have included consent to becontacted to provide further information for researchpurposes on the case they have notified.Data quality is assessed firstly by the veracity of the

user’s details (e.g. the data is treated as the weakestpossible evidence of a disease notification when the reportonly contains the user’s name). The data is treated as mostrobust when a notification contains the user’s name,official NHS email address and General Medical Councilnumber, which are crosschecked with the database.Furthermore, downstream data quality assessment is


undertaken through an automated process to excludemultiple disease notifications during a short period of time(e.g. if the same disease is notified repeatedly within10 minutes or multiple diseases are notified within5 minutes) as such situations are likely to reflect userssimply reading the guidelines for their interest or initiallyto ‘test out’ the app, rather than in relation to a specificpatient. Nevertheless, we have endeavored to minimisethese types of inappropriate notifications, as it is equallyefficient, from a user’s point of view, to not notify, as it isto notify.Specific disease notifications have come from a broad

range of clinical specialities, including General Practice(25%), Emergency Medicine (15%), General Medicine(14%), and Anaesthetics, Paediatrics, Surgery, InfectiousDiseases, Psychiatry, Cardiology, and Neurology.Also, downloads and notifications have been coming

from across the globe, including some resource-poorcountries (Figure 3), and from a wide range of grades,including Foundation Doctors (24%), Medical Trainees(21%), Specialist Registrars (23%), Consultants (18%),and Professors/Lecturers (6%).Our data collection approach allows us to collect both

the denominator data (i.e. which guidelines are beingread by whom) and the numerator data (i.e. specificdisease notifications). Therefore, we are able to monitorusage of the app generally as well as usage of the app fordisease notifications. This allows us to assess data collectedas both simple frequencies and also as a proportion ofusage for that individual user and also as a proportionof usage of the specific disease guideline of interest.Furthermore, these data can be visualised as a global or

Figure 3 Real-time global disease database generation; Live 10.23 am

regional heat-map which can incorporate populationdata, for example disease notifications can be representedas an incidence within a population or even and incidencewithin a population exposed to a particular environmentalrisk factor. Notifications have already provided really im-portant clinical information, for both disease surveillanceand for clinical care quality assessment. Some examples ofdata collected are described in the table (Table 1).The application does not collect any patient-identifiable

demographic data, but rather the data is of the doctor'spractice, which they have agreed to provide at the point ofinstalling the app. In addition, it also has the potential toincrease recruitment into ongoing clinical studies. If adoctor is seeing a patient and reading a guideline for adisease in a hospital in which there is an ongoing study,then an automated system can generate an email to thedoctor and the research nurse for that hospital allowingthem to make contact to consent the patient to be recruitedinto that study (Figure 4). Therefore, no confidential orpatient-identifiable information is made available centrally.During this initial proof-of-concept pilot phase of the

application, notifications from the individual user areavailable for them to review for themselves within theapp itself. However, the wider database was only avail-able to the central research team. Nevertheless, asthere has been growing interest in the potential for thisrich data source to inform future research, we havedeveloped a number of research collaborations, includingwith researchers at the universities of Liverpool, SanDiego and Oxford, through whom we are making thedata relating to the conditions of interest readilyaccessible.

, 5th November 2012.

Table 1 Examples of data collected for disease surveillance and clinical care quality assessment

Notification type Example(s)

Disease Surveillance Three new cases of TB meningitis: one in the UK and two in Pakistan

Diagnosis of rabies in Myanmar

Clinical care quality Of 48 patients with an ST-elevation myocardial infarction; seven received intravenous nitrates;only two received thrombolysis

Of 21 patients with exacerbations of chronic obstructive pulmonary disease; five requirednon-invasive ventilation; only two were able to be managed in the community

Of the eight patients with dementia, only three were prescribed an anticholinergic

Of five patients with HIV, one required 2nd line therapy for drug-resistant disease

Assessing dissemination and education 23 doctors assessed a patient with suspected meningitis, decided whether to perform alumbar puncture and then watched the video on how to do it


However, this approach to data collection is reliant ondoctors using clinical guidelines. This will vary betweendisease areas in response to many factors, such asdisease complexity or rarity, new guideline availability,individual clinician experience with the disease in questionand available time. In addition, doctors do not typically

Figure 4 Schematic demonstrating data transfer of content and repo

refer to guidelines for every patient they see, particularlyin areas where they are able to practice without seekingfurther information, and in these areas our data collectionwill be more limited. Nevertheless, clinicians regularly useguidelines and other reference texts to access informationthat is difficult to remember, such as drug dosages, which

rts between users, researchers and the central team.


specific investigations are most sensitive and specific, ordiseases that are not routinely seen. Furthermore, routineuse of clinical guidelines, and indeed audit of their use,has an increasingly important role in day-to-day clinicalpractice. Moreover, as it does not require any additionaltime to notify a disease once they are reading the guideline,it is hoped that, if even only for specific disease areas,some useful data will be collected. Nevertheless, as noveltechnologies start to generate a new type of epidemio-logical data, new approaches to understanding and auto-mating how best to interpret these data, includingalgorithm generation, will be required [22,23].Future versions of the application will need to be

developed for other smartphone platforms, such as theGoogle Android© market. As many doctors use smartphones on a daily basis there is at least the potential toaccess a wide cohort from whom to collect data. Moreover,future iterations could provide information for, and accessdata from, allied health care professionals, scientists andthe wider public.

ConclusionsIt is unclear at this very early stage if this sort ofapproach can improve disease surveillance and clinicalpractice research, or indeed how many doctors wouldhave to use such an app to create the critical mass tocollected meaningful data. Further studies are needed toassess the data collected through these approaches inparallel with that collected through ongoing studies androutine surveillance approaches.Nevertheless, current approaches appear to miss many

cases and novel approaches, such as ClickClinica ©, mayprovide data to start to address this.

Availability and requirementsThe ClickClinica© database is held centrally and passwordencrypted. However, research, guideline-development andcare quality groups can apply to access data for specificguidelines and diseases, and can also apply to includeresearch questions or guidelines of interest to them infuture ClickClinica © application updates.

Competing interestsThe author has no competing financial interests to disclose, the smartphoneapplication, ClickClinica©, described is made freely available for download.Following the submission of this manuscript, BDM has been awarded a researchgrant from the Institute of Infection and Global Health to utilise the app to assistwith patient recruitment into studies of meningitis and gastrointestinal infection.BDM is an NIHR Doctoral Research Fellow.

Authors’ contributionsThe application, ClickClinica © was conceived by BDM who also led thedevelopment of the application, including collating the guidelines,developing the research questions and analysing the data. The applicationwas developed in collaboration with DG, who also had significantintellectual input in response to peer-review and the revision of thismanuscript for publication and approved the final manuscript. Both authorsread and approved the final manuscript.

AcknowledgementsDr Terry Payne, James Gittins and Richard Crew all helped with thedevelopment of early prototypes. Professor Sarah O’Brien, Dr Fiona McGill, DrDavid Joel Stoeter, Dr Benjamin Duncan, Dr Robert Cooper and Dr AidanMagrath assisted with guideline identification and pilot assessments. Theauthors also acknowledge the assistance of Andy Roberts, Digital MediaStudio ©, for development of multimedia content within the app and, alongwith Professor Tom Solomon, for useful discussions.

Author details1Institute of Infection and Global Health, University of Liverpool, 1st Floor,Ronald Ross Building, 8 West Derby Street, Liverpool L69 7BE, UK.2Department of Computer Science, University of Liverpool, Ashton Building,Ashton Street, Liverpool L69 3BX, UK.

Received: 6 November 2012 Accepted: 26 June 2013Published: 2 July 2013

References1. Sintchenko V, Gallego B: Laboratory-guided detection of disease

outbreaks: three generations of surveillance systems. Arch Pathol Lab Med2009, 133(6):916–925.

2. Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD,O’Brien MA, Johansen M, Grimshaw J, Oxman AD: Audit and feedback:effects on professional practice and healthcare outcomes. CochraneDatabase Syst Rev 2012, 13:6.CD000259.

3. [http://www.hpa.org.uk/webw/HPAweb&HPAwebStandard/HPAweb_C/1317137711190]. Accessed 25th March 2013.

4. Tam CC, Rodrigues LC, Viviani L, Dodds JP, Evans MR, Hunter PR, Gray JJ,Letley LH, Rait G, Tompkins DS, O‘Brien SJ: Longitudinal study of infectiousintestinal disease in the UK (IID2 study): incidence in the communityand presenting to general practice. Gut 2012, 61(1):69–77.doi:10.1136/gut.2011.238386.

5. [http://www.hpa.org.uk/webc/HPAwebFile/HPAweb_C/1251473364307],(accessed 25th March 2013).

6. Michael BD, Sidhu M, Stoeter D, Roberts M, Beeching NJ, Wilkins E, Hart I,Kneen R, Miller A, Solomon T: The epidemiology and management of adultsuspected central nervous system infections - a retrospective cohort studyin the NHS Northwest Region. Q J Med 2010, 103(10):749–758.

7. Michael BD, Menezes BF, Cunniffe J, Miller A, Kneen R, Francis G, BeechingNJ, Solomon T: The effect of delayed lumbar punctures on the diagnosisof acute bacterial meningitis in adults. Emerg Med J 2010, 27(6):433–438.

8. [http://www.kingsfund.org.uk/projects/general-election-2010/faqs#hospital](accessed 25th March 2013).

9. Brabazon ED, O‘Farrell A, Murray CA, Finnegan P: Trends in viral meningitishospitalisations and notifications in the North Eastern Health Board(1997–2001): a cause for concern? Irish Med J 2004, 97(10):306–308.

10. Rotbart HA: Enteroviral infections of the central nervous system.Clin Infect Dis 1995, 20:971–981.

11. Chadwick D: Viral meningitis. Br Med Bull 2005, 75–76:1–14.12. Thigpen MC, Whitney CG, Messonnier NE, Zell ER, Lynfield R, Hadler JL,

Harrison LH, Farley MM, Reingold A, Bennett NM, Craig AS, Schaffner W,Thomas A, Lewis MM, Scallan E, Schuchat A: Emerging infections programsnetwork. Bacterial meningitis in the United States. N Engl J Med 2011,364(21):4–2016.

13. Magazzini S, Nazerian P, Vanni S, Paladini B, Pep G, Casanova B, Crugnola C,Grifoni S: Clinical picture of meningitis in the adult patient and itsrelationship with age. Int Emerg Med 2012, 7(4):359–364.

14. [http://wwwn.cdc.gov/nndss/script/history.aspx] (accessed 25th March 2013).15. Michael BD, Powell GA, Hatch S, Bailey L, Almond S, McGill F, Cousins D,

Hart IJ, Griffiths M, Kneen R, Solomon T: Improving the diagnosis of centralnervous system infections in adults through introduction of a simplelumbar puncture pack: a quality improvement project. Emerg Med J 2013,30(5):402–405. Epub ahead of print. PMID: 22707473.

16. Milburn-McNulty P, Michael BD, Moxton N, Fletcher NA, Crooks D: How todo it: how to get the most out of cerebrospinal fluid cytology. PractNeurol 2012, 12:241–243.

17. Bell DJ, Suckling R, Rothburn MM, Blanchard T, Stoeter D, Michael BD,Cooke RPD, Kneen R, Solomon T: Management of suspected herpessimplex virus encephalitis in adults in a UK teaching hospital.Clin Med 2009, 9(3):231–235.

http://www.hpa.org.uk/webw/HPAweb&HPAwebStandard/HPAweb_C/1317137711190

http://www.hpa.org.uk/webw/HPAweb&HPAwebStandard/HPAweb_C/1317137711190

http://dx.doi.org/10.1136/gut.2011.238386

http://www.hpa.org.uk/webc/HPAwebFile/HPAweb_C/1251473364307

http://www.kingsfund.org.uk/projects/general-election-2010/faqs#hospital

http://wwwn.cdc.gov/nndss/script/history.aspx


18. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L:Detecting influenza epidemics using search engine query data.Nature 2009, 457(7232):1012–1014.

19. Shaman J, Karspeck A: Forecasting seasonal outbreaks of influenza. ProcNatl Acad Sci USA 2012, 109(50):20425–20430.

20. [http://group.bmj.com/products/mobile-apps] (accessed November 2012).21. Boulos MN, Wheeler S, Tavares C, Jones R: How smartphones are changing

the face of mobile and participatory healthcare: an overview, withexample from eCAALYX. Biomed Eng Online 2011, 10:24.doi:10.1186/1475-925X-10-24.

22. Vidal Rodeiro CL, Lawson AB: Online updating of space-time diseasesurveillance models via particle filters. Stat Methods Med Res 2006,15(5):423–444.

23. Wang X, Zeng D, Seale H, Li S, Cheng H, Luan R, He X, Pang X, Dou X,Wang Q: Comparing early outbreak detection algorithms based on theiroptimized parameter values. J Biomed Inform 2010, 43(1):97–103.

doi:10.1186/1472-6947-13-70Cite this article as: Michael and Geleta: Development of ClickClinica: anovel smartphone application to generate real-time global diseasesurveillance and clinical practice data. BMC Medical Informatics andDecision Making 2013 13:70.

Submit your next manuscript to BioMed Centraland take full advantage of:

• Convenient online submission

• Thorough peer review

• No space constraints or color figure charges

• Immediate publication on acceptance

• Inclusion in PubMed, CAS, Scopus and Google Scholar

• Research which is freely available for redistribution

Submit your manuscript at www.biomedcentral.com/submit

http://group.bmj.com/products/mobile-apps

http://dx.doi.org/10.1186/1475-925X-10-24

development of clickclinica: a novel smartphone application to …€¦ · development of...

Documents